# Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark

arXiv: https://arxiv.org/abs/2112.12074 · HTML: https://mlsanity.com/paper/2112.12074/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning](https://mlsanity.com/paper/1802.09232/) (2018)
- [LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images](https://mlsanity.com/paper/1803.00455/) (2018)
- [Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis](https://mlsanity.com/paper/2109.14306/) (2021)

## Built on by (most cited first, 3 of 3)

- [Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022](https://mlsanity.com/paper/2301.13576/) (2023)
- [Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms](https://mlsanity.com/paper/2302.02752/) (2023)
- [Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks](https://mlsanity.com/paper/2302.02755/) (2023)

## Abstract (arXiv)

This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored according to the addressed subtask. The method has the purpose of helping the participants to solve the task and is not meant to reach stateof-the-art performance. Still, for the detection task, the baseline is performing better than the other participants, which stresses the difficulty of such a task.